
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "41"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 117 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 112 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.089492	-0.884712	4.074035	1.278401	0.226614	0.256887	1.901369	0.219413	0.7722	0.7644	0.2385	16.135735	15.349914
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	15.59%	-0.453774	-0.436270	0.737625	-0.509415	1.014505	1.815815	-0.776527	0.952207	0.7024	0.6897	0.2204	3.282906	3.187613
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.358570	-0.959881	1.139529	1.193266	2.255375	-0.342765	1.036897	-0.109389	0.0352	0.0307	-0.0001	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	5.41%	-0.664094	-0.587850	0.829104	-0.422220	1.568189	-0.333419	-0.506203	0.193062	0.6047	0.6085	0.3258	1.932900	1.886335
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	8.11%	-0.123808	-0.390760	1.222180	0.372858	1.893488	-0.045271	-0.463502	0.596476	0.6060	0.6063	0.3189	2.421531	2.260505
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.220358	-0.736042	4.484950	0.237535	0.948313	-1.047329	1.265596	0.044208	0.6764	0.6616	0.2438	7.003883	6.441840
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	5.91%	0.681499	-0.774194	0.838684	0.382149	1.075125	0.941702	-0.657361	0.218625	0.7562	0.7352	0.2026	6.435374	4.673031
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	-0.609773	-0.642708	7.508298	-0.969939	-23.333991	-23.858603	-4.675000	-4.393628	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	2.16%	47.03%	-0.378042	-0.734847	0.393467	-0.459624	-0.593622	-1.048250	-0.641476	1.944980	0.6146	0.6217	0.3130	3.485026	3.146556
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.020160	0.457149	0.148942	0.357839	-0.139509	0.585665	0.301817	0.226823	0.0367	0.0328	-0.0002	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.461421	3.084944	8.515761	0.567552	10.369421	2.389885	24.621371	26.366300	0.5646	0.5657	0.3185	3.250189	2.183264
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	22.04%	0.076469	-0.264746	2.972287	0.702321	0.631690	0.274054	-1.021842	0.086848	0.6137	0.6136	0.3376	2.227840	2.228366
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	7.57%	36.22%	0.207257	-0.200039	3.150049	0.948654	0.099277	-0.564727	-1.230813	-0.436704	0.5998	0.5969	0.3396	2.322703	2.189502
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	60.54%	0.521745	-0.048673	2.899689	0.722981	-0.123678	-0.703566	-0.856880	-0.005023	0.6194	0.6162	0.3354	3.171055	2.729573
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	12.90%	0.559560	-0.082675	3.715940	0.780306	0.020121	0.116060	-0.969764	1.054708	0.6152	0.6190	0.3336	2.301206	2.173113
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.589766	-0.762198	0.578310	-0.713847	1.614366	-0.610221	-0.484521	1.514631	0.0322	0.0292	0.0010	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	48.11%	0.344459	0.024030	2.980391	0.730814	0.138326	0.547758	-0.906689	-0.102128	0.6186	0.6214	0.3327	3.306787	3.087090
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	5.91%	0.00%	0.088850	0.218965	2.298406	1.029397	0.377993	1.301931	-1.132495	1.177682	0.6200	0.6239	0.3371	1.199991	1.188104
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	98.92%	1.08%	0.120308	-0.255269	2.058510	0.815817	0.577938	-0.522832	1.670023	0.833138	0.6989	0.6967	0.3004	0.000000	0.000000
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	7.57%	0.471289	0.097966	1.934579	0.790738	0.877425	1.614231	0.332814	2.600768	0.6250	0.6288	0.3340	1.575281	1.490865
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.694598	0.673128	4.436115	1.844614	0.051497	0.449033	-1.157348	1.031529	0.6166	0.6165	0.3409	16.342210	3.108419
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	8.65%	0.629655	0.198457	2.252381	1.159667	-0.141356	-0.119419	-1.401508	-0.106419	0.6169	0.6201	0.3389	1.412972	1.364433
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.852630	-0.844525	0.847402	-1.070183	4.631469	-1.024717	-0.541475	0.179780	0.0324	0.0305	0.0007	nan	nan
2459570	digital_ok	100.00%	0.00%	29.19%	0.00%	100.00%	0.00%	-0.109850	1.896492	-0.164869	-0.076140	-0.386038	42.290510	-0.905230	1.543662	0.6065	0.4812	0.3352	6.556636	5.608786
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	1.62%	-0.705566	0.278407	2.497751	1.643501	1.835325	1.648433	1.658673	1.930263	0.7278	0.7283	0.2070	2.003854	1.834602
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.492338	-0.170659	2.900627	2.000615	0.388134	1.541548	-0.153329	2.053984	0.6065	0.6143	0.3541	0.000000	0.000000
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	95123.740823	135470.389219	5044120.835382	4059034.956043	nan	nan	-5.052525	-5.052525	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	2.31%	2.872942	0.669594	3.216732	0.011690	-0.071625	-1.586541	-1.609977	0.030304	0.6189	0.6302	0.3359	2.017915	1.668312
2459563	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	2.70%	1.674287	0.010243	1.118430	-0.454100	-0.240339	-1.970312	-1.330601	-1.029471	0.6073	0.6222	0.3399	1.714763	1.577667
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	17.30%	2.129819	0.404564	1.597104	-0.008599	-0.201461	-1.335590	-1.862615	-1.713307	0.6135	0.6332	0.3389	1.664733	1.503470
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	5.41%	13.51%	1.612887	0.160908	1.218334	-0.216198	-0.195585	-0.853589	-1.569839	-0.507330	0.5922	0.6075	0.3504	1.662561	1.507450
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	40.32%	2.444524	0.635223	1.850259	0.255194	0.186189	-0.785971	-1.390226	-0.815532	0.6186	0.6336	0.3300	2.147707	1.907095
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.385466	4.146300	12.983780	9.207555	12.922852	10.016350	-1.369845	-0.716745	0.6147	0.6242	0.3367	4.300726	3.746879
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0387	0.0357	0.0018	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.106290	4.149000	13.666831	9.204486	12.184951	8.024361	-1.994161	-2.678824	0.6254	0.6331	0.3300	4.561584	3.751955
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6000	0.6127	0.3419	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6150	0.6259	0.3384	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.287556	4.843364	17.952050	13.347366	18.910430	14.477140	0.584828	0.655463	0.6300	0.6377	0.3366	-0.000000	-0.000000
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	44.09%	1.939330	0.525168	1.705037	-0.139615	-0.105772	-1.824738	-1.884962	-0.802414	0.6186	0.6283	0.3308	2.252600	1.889536
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0370	0.0323	0.0007	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.512001	4.130627	13.228966	9.647978	13.055306	8.954308	-1.734011	1.667016	0.6227	0.6312	0.3404	3.044451	2.619818
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.987967	4.468110	26.288233	20.780853	0.552994	0.661721	-1.903978	-0.920039	0.6846	0.6864	0.3129	3.898616	3.296184
2459541	digital_ok	100.00%	1.79%	1.79%	0.00%	100.00%	0.00%	6.681668	3.629822	16.610524	12.257721	10.261940	9.144597	4.533905	3.713393	0.6809	0.6795	0.2875	2.727705	2.462296
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	30.36%	3.319481	1.786710	3.021598	1.797691	1.420439	-0.168414	-1.041146	-0.739990	0.6872	0.6818	0.3085	1.866809	1.730141
2459536	digital_ok	100.00%	37.06%	37.06%	0.00%	100.00%	0.00%	10.105150	7.763855	22.077306	17.813574	20.644682	16.666591	-2.978407	-3.271594	0.4174	0.4152	0.2144	2.688608	2.166356
2459535	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.404237	1.294501	2.380734	1.556761	2.979727	-0.472700	-0.214662	0.167579	0.0656	0.0553	0.0085	1.185921	1.191963
2459534	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.653404	0.797476	1.426787	0.614458	0.427101	-1.164275	-1.256786	0.454459	0.0783	0.0610	0.0091	1.209058	1.216455
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.118950	4.957682	13.498121	10.889704	11.053983	10.151343	-2.530558	-1.865411	0.0682	0.0566	0.0083	1.226223	1.228489
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.422821	5.696205	17.741740	13.776835	14.417049	12.304297	-1.936557	-1.756887	0.0677	0.0532	0.0085	1.155945	1.162377
2459530	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.817509	0.631241	1.383544	0.194226	0.007288	-1.032525	-1.521481	-0.732918	0.0768	0.0578	0.0110	1.183344	1.195448
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.224115	5.929302	14.180853	11.326187	29.672548	19.591914	-1.307160	0.433162	0.9021	0.9329	0.2947	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	7.777402	5.742013	19.600608	15.610786	12.774374	13.801821	-5.408791	-4.091351	nan	nan	nan	9.183072	8.983938
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.677064	4.454634	17.932267	13.275650	7.385441	10.846458	-2.258599	-0.812408	0.6980	0.6648	0.3292	3.272464	3.613563
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	5.143941	3.753582	9.387356	5.830743	7.238965	1.284299	2.214044	4.395054	0.0416	0.0504	0.0007	nan	nan
2459508	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	6.766455	7.104593	11.359357	9.404466	12.668081	8.259067	10.173924	10.623054	0.0823	0.0583	0.0033	0.000000	0.000000
2459505	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	194.372287	106.545464	113.671060	106.758375	83.626374	81.246286	484.647166	425.422578	0.0113	0.0102	0.0042	1.092005	1.095548

Antenna Season Report Notebook¶

Antenna 41 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 41, Node 4:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 41: 2459645

Antenna 41: 2459642

Antenna 41: 2459640

Antenna 41: 2459639

Antenna 41: 2459638

Antenna 41: 2459637

Antenna 41: 2459634

Antenna 41: 2459633

Antenna 41: 2459632

Antenna 41: 2459631

Antenna 41: 2459630

Antenna 41: 2459629

Antenna 41: 2459628

Antenna 41: 2459627

Antenna 41: 2459626

Antenna 41: 2459625

Antenna 41: 2459624

Antenna 41: 2459623

Antenna 41: 2459622

Antenna 41: 2459621

Antenna 41: 2459620

Antenna 41: 2459619

Antenna 41: 2459618

Antenna 41: 2459617

Antenna 41: 2459616

Antenna 41: 2459615

Antenna 41: 2459614

Antenna 41: 2459613

Antenna 41: 2459612

Antenna 41: 2459611

Antenna 41: 2459610

Antenna 41: 2459609

Antenna 41: 2459608

Antenna 41: 2459607

Antenna 41: 2459605

Antenna 41: 2459604

Antenna 41: 2459603

Antenna 41: 2459602

Antenna 41: 2459598

Antenna 41: 2459597

Antenna 41: 2459596

Antenna 41: 2459595

Antenna 41: 2459594

Antenna 41: 2459593

Antenna 41: 2459592

Antenna 41: 2459591

Antenna 41: 2459590

Antenna 41: 2459589

Antenna 41: 2459588

Antenna 41: 2459587

Antenna 41: 2459586

Antenna 41: 2459585

Antenna 41: 2459584

Antenna 41: 2459583

Antenna 41: 2459582

Antenna 41: 2459581

Antenna 41: 2459580

Antenna 41: 2459579

Antenna 41: 2459578

Antenna 41: 2459577

Antenna 41: 2459576

Antenna 41: 2459575

Antenna 41: 2459574

Antenna 41: 2459573

Antenna 41: 2459572

Antenna 41: 2459571

Antenna 41: 2459570

Antenna 41: 2459569

Antenna 41: 2459566

Antenna 41: 2459565

Antenna 41: 2459564

Antenna 41: 2459563

Antenna 41: 2459562

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶